Prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy: a systematic review and external validation study

Abstract

To cite this article: Hilkens NA, Algra A, Greving JP. Prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy: a systematic review and external validation study. J Thromb Haemost 2016; 14: 167-74. EssentialsPrediction models may help to identify patients at high risk of bleeding on antiplatelet therapy. We identified existing prediction models for bleeding and validated them in patients with cerebral ischemia. Five prediction models were identified, all of which had some methodological shortcomings. Performance in patients with cerebral ischemia was poor.Summary. Background: Antiplatelet therapy is widely used in secondary prevention after a transient ischemic attack (TIA) or ischemic stroke. Bleeding is the main adverse effect of antiplatelet therapy and is potentially life threatening. Identification of patients at increased risk of bleeding may help target antiplatelet therapy. Objective: This study sought to identify existing prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy and evaluate their performance in patients with cerebral ischemia. Methods: We systematically searched PubMed and Embase for existing prediction models up to December 2014. The methodological quality of the included studies was assessed with the CHARMS checklist. Prediction models were externally validated in the European Stroke Prevention Study 2, comprising 6602 patients with a TIA or ischemic stroke. We assessed discrimination and calibration of included prediction models. Results: Five prediction models were identified, of which two were developed in patients with previous cerebral ischemia. Three studies assessed major bleeding, one studied intracerebral hemorrhage and one gastrointestinal bleeding. None of the studies met all criteria of good quality. External validation showed poor discriminative performance, with c-statistics ranging from 0.53 to 0.64 and poor calibration. Conclusion: A limited number of prediction models is available that predict intracranial hemorrhage or major bleeding in patients on antiplatelet therapy. The methodological quality of the models varied, but was generally low. Predictive performance in patients with cerebral ischemia was poor. In order to reliably predict the risk of bleeding in patients with cerebral ischemia, development of a prediction model according to current methodological standards is needed.

Clinical Prediction Models for Primary Prevention of Cardiovascular Disease: Validity in Independent Cohorts

“…Similar reductions in discrimination have been reported previously for other diseases. [18][19][20] Poor discriminative ability may be attributable to model invalidity (biased estimates of regression coefficients) or to a less heterogeneous patient case-mix (identifying high and low risk individuals is more difficult if the population is more homogenous). The MB-c is a useful tool to distinguish these sources because it estimates the discriminative ability of the model in a new cohort based solely on the case-mix in that cohort.…”

Clopidogrel Drug Interactions and Serious Bleeding: Generating Real‐World Evidence via Automated High‐Throughput Pharmacoepidemiologic Screening

“…Time‐invariant covariates (e.g., genetic polymorphisms, race, ethnicity) are inherently controlled for by the self‐controlled case series design . In each regression model, we included the following time‐varying covariates putatively associated with serious bleeding risk among antiplatelet drug users, as adapted from S 2 TOP‐BLEED and its predecessors: (i) prior history of or current ischemic heart disease and/or cerebrovascular disease; (ii) prior gastrointestinal bleeding and/or intracranial hemorrhage; (iii) ongoing concomitant therapy with an anticoagulant (e.g., warfarin), a non‐clopidogrel antiplatelet drug (e.g., aspirin), a gastroprotective agent (e.g., lansoprazole), and/or an NSAID (e.g., ibuprofen); and (iv) average daily dispensed object drug dose. Because some of these drugs are available without a prescription, reliance on claims data may lead to underascertainment.…”

Refining prediction of major bleeding on antiplatelet treatment after transient ischaemic attack or ischaemic stroke

“…Candidate predictors that could potentially improve prediction of bleeding were selected based on the literature. 16 Their inclusion was dependent on availability in OXVASC. Predictors of interest that could be studied were history of peptic ulcer 7,8 (based on face-to-face patient interview plus cross-referencing with primary care and medical records), history of cancer 9,10 (based on face-to-face patient interview plus cross-referencing with primary care and medical records).…”